DETAILED ACTION
Status of Application
This action is a Final Rejection. This action is in response to the amendment and response filed on May 4, 2026.
Claims 1, 6, 10, 11, 16, and 18 have been amended.
Claims 1-20 are pending and rejected.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that “the subject matter of the amended independent claims is similar to that found to be eligible subject matter in Example 39 of the 2019 PEG, which relates to ‘training a neural network for facial detection.’” Remarks at 8. However, the claim in this example was found eligible at step 2A, prong one, because a judicial exception was not recited. The example does not include any analysis under steps 2A, prong two, or step 2B.
Applicant further argues that the claims are similar to those found eligible in Enfish. Remarks at 8. In Enfish, “the features were not conventional and thus were considered to reflect an improvement to existing technology. In particular, they enabled the claimed table to achieve benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements.” MPEP 2106.05(d). A similar improvement to technology is not provided in the instant claims. Determining contradictions between stated company values and actual activities does not provide an improvement to technology or a technological field.
Applicant further argues that “[t]he amended claims recite the specific technical steps of the closed-loop process, retraining using operational results, generating a revised model from improved training data, and composing the recommendation using that revised model, rather than merely claiming the result of an improved recommendation.” Remarks at 9. Applicant further refers to paragraphs 0050 and 0051 of the Specification. Id. However, Applicant has not shown that the machine learning technology has been improved. Instead, Applicant is using existing technology to improve an abstract idea.
Additionally, Applicant’s claims are ineligible for reasons similar to claim 2 of Example 47. In both the instant claims and the example, the computer is recited at a high level of generality and is being used as a tool to perform an abstract idea.
As such, the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03)
Yes, with respect to claims 1-9, which recite a method and, therefore, are directed to the statutory class of process.
Yes, with respect to claims 10-15, which recite a system and, therefore, are directed to the statutory class of machine or manufacture.
Yes, with respect to claims 16-20, which recite a non-transitory computer-readable storage medium and, therefore, are directed to the statutory class of manufacture.
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a))
The following claims identify the limitations that recite the abstract idea in regular text and that recite additional elements in bold:
1. A computer-implemented method comprising:
receiving, by a computer system, a user input indicating values or interests of a user;
analyzing, by the computer system, the user input to locate and extract preference data from the user input;
tracking, by the computer system, activities of the user;
determining, by the computer system using machine learning including a machine learning model, differences between the preference data and the tracked activities to illustrate contradictions by the user;
retraining, by the computer system, the machine learning model based on training feature data using a closed loop system, wherein results obtained using the machine learning model during operation are used to improve the training data;
generating, by the computer system, a revised machine learning model using the improved training data;
composing, by the computer system using the revised machine learning model, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
displaying, on a graphical user interface in communication with the computer system, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
2. The computer-implemented method of claim 1, wherein the user input is related to sustainability ratings of an exchange-traded fund (ETF).
3. The computer-implemented method of claim 1, wherein the activities of the user include investment activities of the user.
4. The computer-implemented method of claim 3, further comprising: automatically adjusting, by the computer system using the machine learning, a user investment portfolio based on the recommendation.
5. The computer-implemented method of claim 1, wherein displaying the recommendation includes displaying a list of differences between the preference data and the tracked activities to illustrate contradictions by the user.
6. The computer-implemented method of claim 1, wherein the machine learning model includes one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.
7. The computer-implemented method of claim 1, wherein displaying the recommendation includes displaying an article recommendation for a news article related to the activities of the user.
8. The computer-implemented method of claim 7, wherein displaying the article recommendation includes displaying the news article.
9. The computer-implemented method of claim 8, wherein displaying the news article includes composing, by the computer system using the machine learning, the news article.
10. A system comprising:
a computing system comprising one or more processors and a data storage system in communication with the one or more processors, wherein the data storage system comprises instructions thereon that, when executed by the one or more processors, causes the one or more processors to:
receive a user input indicating values or interests of a user;
analyze the user input to locate and extract preference data from the user input;
track activities of the user;
determine, using machine learning including a machine learning model, differences between the preference data and the tracked activities to illustrate contradictions by the user;
retrain the machine learning model based on training feature data using a closed loop system, wherein results obtained using the machine learning model during operation are used to improve the training data;
generate a revised machine learning model using the improved training data;
compose, using the revised machine learning model, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
display, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
11. The system of claim 10, wherein the machine learning model includes a neural network.
12. The system of claim 11, wherein the neural network includes a long short-term memory (LSTM) network.
13. The system of claim 10, wherein the machine learning includes bidirectional encoder representations from transformers (BERT).
14. The system of claim 10, wherein the machine learning includes natural language processing (NLP).
15. The system of claim 10, wherein the machine learning includes an artificial intelligence (AI)-based knowledge tree.
16. A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium including instructions that, when executed by computers, cause the computers to perform operations of:
receiving a user input indicating values or interests of a user;
analyzing the user input to locate and extract preference data from the user input;
tracking activities of the user;
determining, using machine learning including a machine learning model, differences between the preference data and the tracked activities to illustrate contradictions by the user;
retraining the machine learning model based on training feature data using a closed loop system, wherein results obtained using the machine learning model during operation are used to improve the training data;
generating a revised machine learning model using the improved training data;
composing, using the revised machine learning model, a recommendation for the user based on the determined differences, wherein composing the recommendation includes using the machine learning to determine contradictions between stated company values and actual company activities to identify companies with publicly available ratings that do not align with company activity, and automatically generating an independent rating for at least a portion of a user investment; and
displaying, on a graphical user interface, the recommendation to assist the user in aligning the activities of the user with the values or interests of the user.
17. The non-transitory computer-readable storage medium of claim 16, wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of: providing an alert to the user based on the determined differences.
18. The non-transitory computer-readable storage medium of claim 16, wherein the machine learning model includes one or more of a long short-term memory (LSTM) network, bidirectional encoder representations from transformers (BERT), natural language processing (NLP), or an artificial intelligence (AI)-based knowledge tree.
19. The non-transitory computer-readable storage medium of claim 16, wherein the activities of the user include investment activities of the user.
20. The non-transitory computer-readable storage medium of claim 19, wherein the medium further includes instructions that, when executed by computers, cause the computers to perform operations of: automatically adjusting, using the machine learning, a user investment portfolio based on the recommendation.
Yes. But for the recited additional elements as shown above in bold, the remaining limitations of the claims recite certain methods of organizing human activity. The claims are directed to recommending investments to a user that align with the user’s values. This type of method of organizing human activity is a fundamental economic practice because it involves investing and a commercial interaction such as agreements in the form of contracts, advertising, marketing or sales activities or behaviors, and business relations. Thus, the claims recite an abstract idea.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d))
No. The claims as a whole merely use a computer as a tool to perform the abstract idea. The computing components (i.e., additional elements that are in bold above) are recited at a high level of generality and are merely invoked as a tool to implement the steps. For example, only a programmed general purpose computing device is needed to implement the claimed process. Additionally, the use of machine learning is recited at a high level. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, there is no improvement to the functioning of a computer or technology. Therefore, the abstract idea is not integrated into a practical application.
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05)
No. As discussed with respect to Step 2A, Prong 2, the additional elements in the claims, both individually and in combination, amount to no more than tools to perform the abstract idea. Merely performing the abstract idea using a computer cannot provide an inventive concept. Therefore, the claims do not provide an inventive concept.
As such, the claims are not patent eligible.
Relevant Prior Art
The following references are relevant to Applicant’s invention:
Jain, U.S. Patent Application Publication Number 2023/0385934 A1. This reference teaches a method for providing financial recommendations for investors with preferences for non-financial characteristics.
Wirth et al., U.S. Patent Application Publication Number 2012/0016807 A1. This reference teaches a personalized financial illustration system.
Pathak et al., U.S. Patent Application Publication Number 2020/0160442 A1. This reference teaches generating company ratings based on sustainability.
Ahlstrom et al., U.S. Patent Application Publication Number 2022/0138856 A1. This reference teaches an asset recommendation system that allows users to identify and invest based on their values, interests, and passions.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH H ROSEN whose telephone number is (571) 270-1850 and email address is elizabeth.rosen@uspto.gov. The examiner can normally be reached Monday - Friday, 10 AM ET - 7 PM ET.
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/ELIZABETH H ROSEN/Primary Examiner, 3693
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